simonorozcoarias / ML_DL_microArrays
Here, we describe the comparison of the most used algorithms in classical ML and DL to classify carcinogenic tumors described on 11_tumor data base, obtaining accuracies between 76.97% and 100% for tumor identification. Our results bring up a more efficient an accurate classification method based on gene expression (microarray data) and ML/DL al…
☆10Updated 4 years ago
Related projects: ⓘ
- This repository contains code used to build and interpret a deep learning model. It is a DNN classifier trained using gene expression dat…☆9Updated 3 years ago
- DCAP:Integrating multi-omics data with deep learning for predicting cancer prognosis☆20Updated 3 years ago
- This is the repository for paper titled as "Convolutional neural network models for cancer type prediction based on gene expression".☆44Updated 5 years ago
- Deep-Learning framework for multi-omic and survival data integration☆74Updated 9 months ago
- OncoNetExplainer: Explainable Prediction of Cancer Types Based on Gene Expression Data☆8Updated last year
- ☆11Updated 8 months ago
- ☆17Updated 3 years ago
- ☆43Updated this week
- ☆47Updated last year
- Pathway-based sparse deep neural network for survival analysis☆36Updated last year
- Clinical data for the TCGA PanCancer Atlas☆12Updated 5 years ago
- ☆28Updated 3 years ago
- Using traditional machine learning and deep learning methods to predict stuff from TCGA pathology slides.☆18Updated 5 years ago
- SALMON: Survival Analysis Learning with Multi-Omics Neural Networks☆67Updated last year
- ALD study data analysis☆13Updated last year
- MOMA: A Multi-task Attention Learning Algorithm for Multi-omics Data Interpretation and Classification☆15Updated last year
- ☆14Updated last year
- ☆33Updated 2 years ago
- Interpretable convolutional neural networks on multi-omics data predict long-term survival in glioblastoma☆13Updated last year
- Detection of pathological colorectal cancer based on deep learning☆10Updated 3 years ago
- Classifying tumor types based on Whole Genome Sequencing (WGS) data☆44Updated 10 months ago
- ☆29Updated this week
- Implementation of DeepSurv using Keras☆46Updated last year
- HiGCN: a hierarchical graph convolution network for representation learning of gene expression data☆11Updated 3 years ago
- Reproducing the experiments of the DestVI paper☆17Updated 2 years ago
- a deep learning approach for integrative cancer subtyping of multi-omics data☆37Updated 4 months ago
- Multi-omics integration method using AE and GCN☆28Updated last year
- Deep learning methods for feature selection in gene expression autism data.☆32Updated 2 years ago
- using shallow neural network layer (embedding) to infer gene-gene/sample relationship from gene expression data☆21Updated 5 years ago
- repository with the scripts to run examples of the publications with new functionalities of TCGAbiolinks☆14Updated 2 years ago